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\documentclass{sig-alternate}
\usepackage{url}
\usepackage{latexsym}
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\usepackage{booktabs}
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\usepackage{multirow,bigdelim}
\usepackage{latexsym}
\usepackage{paralist}
\usepackage{xspace}



\title{Named Entity Recognition for Novel Types \\by Transfer Learning}

% Max 8 pp.

\begin{document}
\maketitle

\begin{abstract}
We present a deep learning based transfer learning model to learn
a set of Named Entity types from a small amount of
training data by leveraging a large training corpus
annotated with some related, but different Named
Entity types.
Our study focus in the main aspects of transfer learning, and in particular 
a in transfer learning between NER systems that have a different label distribution: (i) what to transfer?, (ii) how to transfer?, and (iii) when to transfer?
We found that... 

(i) what to transfer?
feature representation transfer (supervised and unsupervised feature construction) vs. instance transfer vs. transferring knowledge of parameters (prior distribution of parameters) vs. transferring relational knowledge (statistical learning, relational learning techniques)

We transfer feature weights or feature representations
Why we are not targeting other transfer options?

(ii) how to transfer?


and (iii) when to transfer?

Abstract features vs. specific features in a NN


\end{abstract}

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\newcommand{\lizhen}[1]{\textcolor{green}{#1}}

\newcommand{\dataset}[1]{\texttt{#1}\xspace}
\newcommand{\Conll}{\dataset{CoNLL}}
\newcommand{\Ib}{\dataset{I2b2}}   				% cannot put number in the command...
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\newcommand{\lake}{\type{LOCATION:Lake, Sea, Ocean}}
\newcommand{\locOther}{\type{LOCATION:Other}}
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\newcommand{\misc}{\type{MISC}}

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\newcommand{\orgGov}{\type{ORGANIZATION:Government}}
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\newcommand{\orgDescGov}{\type{ORGANIZATION:Desc:Government}}
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\newcommand{\orgEducational}{\type{ORGANIZATION:Political}}

\newcommand{\person}{\type{PERSON}}
\newcommand{\age}{\type{AGE}}
\newcommand{\dates}{\type{DATE}} %%% language is reserved word
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\newcommand{\gpeDescCountry}{\type{GPE:Desc:Country}}
\newcommand{\gpeDescOther}{\type{GPE:Desc:Other}}
\newcommand{\lang}{\type{LANGUAGE}} 			%%% language is reserved word
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\newcommand{\ti}{\type{TIME}}              %%% time is reserved word
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\newcommand{\best}[1]{\textbf{#1}}
\newcommand{\ctx}{\ensuremath{\text{ctx}}}

\input{intro}
\input{transferLearning}
\input{ourMethod}
\input{experimentSetup}
\input{results} 
\input{relwork}
\input{conclusion}




\section*{Acknowledgments}

Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\
Anonymised\\

%NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

\bibliographystyle{abbrv}
\bibliography{biblio}

\end{document}
